27 papers · ranked by Valyu relevance
Jiqing Wang, Xiang Wang, Yu Shen, Piero Malcovati + 3 more
This paper designs a hardware-implementable joint denoising and demosaicing acceleration system. Firstly, a lightweight network architecture with multi-scale feature extraction based on partial convolution is proposed at the algorithm level. The partial convolution scheme can reduce the redundancy of filters and…
Andrea Vittimberga, Giovanni Nicolini, Giuseppe Scotti, Xuming Zhang
This paper presents an automated threshold-based multi-channel epileptic seizure detection algorithm designed for low-complexity hardware implementations. The algorithm relies on two discriminative, computationally simple time-domain features, based on power and amplitude variations, that enable accurate and timely…
Hasan Abdel Aziz Mohamed, Mariam Taher, Hossam Moetaz Shousha, Ziad Mohsen + 3 more
The era of the Internet of Things (IoT) introduces new technologies alongside challenges in hardware and data transfer security. This work presents the hardware implementation of the Lightweight Cryptography (LWC) algorithm ASCON-128, specifically targeting IoT applications and edge devices with stringent power and…
Ayesha Waris, Arshad Aziz, Bilal Muhammad Khan, Muhammad E. S. Elrabaa
Post-quantum cryptographic (PQC) algorithms are essential due to the threat posed by quantum computers to the security of currently deployed cryptosystems. CRYSTALS-Kyber, based on Lattice-based cryptography, has been standardized as the Public-Key Encryption and Key-Establishment Mechanism Algorithm by the National…
Francesco Porreca, Fabio Frustaci, Raffaele Gravina, Alfio Dario Grasso
Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer…
Zhengyinan Li, Jing Wu, Mohamed Ibrahem, Junaid Shuja
Autonomous driving perception demands low latency, high temporal resolution, and stringent hardware efficiency. While event-based spiking neural networks (SNNs) offer bio-inspired sparse computation, their deployment on edge field-programmable gate arrays (FPGAs) is obstructed by irregular execution patterns and…
Authors not listed
Solubility is the maximum amount of solutes that can dissolve in a certain amount of solvent at a certain temperature, and it is significant in battery electrolyte research since it confines the design space. Thus, solubility measurement is a critical constraint on running self-driving labs for battery electrolyte…
Ivan Kondratyev, Weinan Sun
AI coding assistants excel at software tasks but lack structured access to laboratory hardware, the physical instruments that define experimental science. We present Ataraxis, an open-source framework that provides hardware control capabilities spanning camera acquisition, microcontroller communication, precision…
Chakrabarti, Aradhya
—Soft-core processors on resource-constrained FPGAs often suffer from low code density and reliance on proprietary toolchains. This paper details the design, implementation, and evaluation of a 32-bit dual-stack microprocessor architecture optimized for low-cost, resource-constrained Field-Programmable Gate Arrays…
Bram F. Haverkort, Aida Todri-Sanial
Computing with coupled oscillators or oscillatory neural networks (ONNs) has recently attracted a lot of interest due to their potential for massive parallelism and energy-efficient computing. However, to date, ONNs have primarily been explored either analytically or through analog circuit implementations. This paper…
Vijay Pratap Sharma, Annu Kumar, Mohd Faisal Khan, Mukul Lokhande + 1 more
—This work presents Bio-RV, a compact and resourceefficient RISC-V processor intended for biomedical control applications, such as accelerator-based biomedical SoCs and implantable pacemaker systems. The proposed Bio-RV is a multi-cycle RV32I core that provides explicit execution control and external instruction…
Maxwell B. Madden, Mahee Khatri, Arnav Mohanty, Disha Prasad + 2 more
Head-fixed behavior in rodents is a foundational technique in systems neuroscience which enables use of sophisticated imaging techniques in combination with animal behavior. However, accessibility of head-fixed behavior techniques is limited. Animal training consumes a large amount of experimenter labor and commercial…
Angelo Barbieri, Christopher A. Flores, Wladimir Valenzuela, Francisco Saavedra + 1 more
Image sensors produce high-dimensional visual data for classification algorithms. Deep Neural Networks (DNNs) achieve high accuracy but require large labeled datasets and computational and energy resources, limiting their use in embedded systems. Active Learning (ALrn) can reduce labeling effort by selecting samples…
Qinglin Yang, Yuan Liu, Yaoyao Zhang, Boya Wang + 3 more
Blockchain systems are undergoing a fundamental transition from decentralized ledgers for digital assets to general-purpose trust infrastructures for verifiable computation, decentralized physical resources, and automated infrastructure management. Meanwhile, the limitations of the Blockchain as a Service (BaaS) model…
Qihang Wu, Austin Rovinski
Priority queues - data structures that serve elements based on priority rather than insertion order - are fundamental in a wide range of applications, including operating systems, graph algorithms, and data compression. Software implementations, typically based on binary heaps with O(log N) complexity, are sufficient…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Brian Pachideh, Sven Nitzsche, Moritz Neher, Jann Krausse + 4 more
Spiking Neural Networks (SNNs) promise significant advantages over conventional Artificial Neural Networks (ANNs) for applications requiring real-time processing of temporally sparse data streams under strict power constraints -- a concept known as the Neuromorphic Advantage. However, the limited availability of…
Amin Saberi, Bin Wan, Kevin J. Wischnewski, Kyesam Jung + 6 more
Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility…
Alexandra Cheng, Tate DeWeese, Yueqing Zhou, Yotaro Sueoka + 6 more
High-density Neuropixels probes enable the study of large neural populations with single-cell and sub-millisecond resolution. While single-probe and acute head-fixed experiments have yielded critical scientific insights, understanding the neural mechanisms underlying many complex behaviors requires simultaneous…
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
Sorting is a fundamental operation across numerous computational domains. Traditionally, this process involves transferring data from main memory to a processing unit for sorting, followed by writing the sorted data back to memory. This conventional approach incurs substantial latency and energy overheads due to the…
Peter M. Kogge
| 1 | | Introduction 2 | | |---|---------|---------------------------------------------------------------|--| | | 1.1 | Organization 2 | | | | 1.2 | Design-specific Papers 2 | | | | 1.3 | Change Log 2 | | | 2 | Surveys | 3 | | | 3 | | Specific System Design Studies 3 | | | | 3.1 | 1969: Cellular Logic in Memory 3 | | |…
Authors not listed
The complete active space self-consistent field (CASSCF) method is essential for describing complex photochemical processes, but its application in ab initio molecular dynamics is often limited by the computational cost associated with four-center two-electron repulsion integrals (ERIs). We present the first…
Diptodip Deb, Gert-Jan Both, Eric Bezzam, Amit Kohli + 26 more
Modern microscopy methods incorporate computational modeling as an integral part of the imaging process, either to solve inverse problems or optimize the optical system design itself. These methods often depend on differentiable optics simulations, yet no standardized framework exists—forcing computational optics…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
Authors not listed
Tandem mass spectrometry (MS/MS) fragmentation is conventionally understood as stochastic bond cleavage determined by thermochemical bond strengths and collision energies. We demonstrate that fragmentation is a deterministic categorical state progression governed by phase-lock network topology, where fragment…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Authors not listed
We present a unified theoretical framework that classifies and analyzes quantum enhancement strategies for classical algorithms, establishing design paradigms that systematically combine quantum subroutines with classical procedures. The theory identifies four fundamental enhancement mechanisms: quantum search…